共查询到17条相似文献,搜索用时 140 毫秒
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在医疗设备研发制造等领域,为了实现人体体温附近范围内高精度的温度测量,设计了一种基于低功耗单片机MSP430、非接触式红外温度传感器MLX90614以及蓝牙传输技术的无线温度监控系统,采集发送模块以超低功耗单片机MSP430为控制核心,通过SMBus协议读取MLX90614所采集到的目标温度值,利用蓝牙透传模块将数据上传.MSP430单片机的使用极大地降低了系统功耗,MLX90614非接触测量方式满足了医疗设备研发污染隔离的特殊需求,具有较高的测量分辨率和精度,蓝牙透传模块的使用使得系统易于实现数字化、网络化和集散化管理.实验表明:该系统在32 ~42℃温度范围内,测量精度达±0.2℃,并实现了测量结果的无线传输,在医疗设备研制领域有广泛的应用前景. 相似文献
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针对铝电解槽侧壁传统温度检测误差大、时间不连续、成本昂贵、信噪比低等问题,设计了一种基于ZigBee和MLX90614非接触式红外温度传感器的铝电解槽侧壁温度监测系统.系统硬件选用CC2530为主芯片,MLX90614为温度测量传感器;系统软件基于TI公司的ZStack-2.5.1a协议栈进行设计,同时开发基于B/S模式(服务器/浏览器模式)的上位机软件帮助工作人员实时查看和分析温度数据.现场测试结果证明:该系统部署方便,网络数据传输可靠,节点采集温度的误差在3 ℃以内,上位机软件正常工作.该系统能够实现铝电解槽侧壁温度的实时监测. 相似文献
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设计了一个基于Arduino Mega 2560单片机可穿戴智能手机壳设备,使用黑白电子墨水屏可视化设备的数据,使用DHT11温度湿度传感器实时测量室内的温度和湿度,MLX90614体温传感器和PULSESENSOR心率传感器为用户提供健康监测,本设备拥有SU-03T语音模块,用户不仅可以语音启用不同的模块,还可以播放MP3。 相似文献
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设计一种基于MSP430单片机的应用于电力设备测温的便携式红外测温仪。采用射频识别(RFID)技术对测量地点进行定位。基于非接触、远距的测量特点,设计采用红外测温传感器MLX90614测量目标温度,同时,利用环境温度传感器采集环境温度,依据红外测温原理对目标温度进行高精度修正。系统内置SD2200时间模块,准确记录测量时间。系统内置USB读写模块,将位置信息、目标温度、环境温度、修正温度、测量时间记录在内置SD卡内,方便导入电脑并生成报表。该便携式测温仪从根本上杜绝电力巡检中漏测、错记的问题,生成报表方便查询,大大提高工作效率。 相似文献
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基于MLX90614的非接触式体温测量系统设计 总被引:1,自引:0,他引:1
根据辐射测温原理设计制作温度测量系统。采用Melexis公司的MLX90614非接触测量的红外温度传感器,通过SMBus协议与AT89S51单片机通讯,并通过单片机系统驱动液晶显示模块显示,实现了非接触的温度测量,测量精度可达到±0.1℃,可广泛地运用于医学、化工等领域。 相似文献
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随着科学的发展与时代的进步,传统的农业已经向着智能化、自动化、信息化的方向发展。本文主要说明设计制造一个基于STM32单片机的生猪生长参数自动检测系统。TFT LCD触摸液晶屏显示与发出指令,射频模块识别每一头生猪,HC-SR04超声波测量生猪的高度,MLX90614红外测温模块测量生猪的体温,HX711重量传感器测量体重并且STM32采集AD数据返回体重值。通过这一系列的模块测量以及STM32对其数据采集,最后可完成这套自动检测系统的设计,实现生猪检测自动化、智能化。 相似文献
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本文以STM32F103单片机为控制芯片,利用MLX90614红外测温传感器和MKB0805脉搏血压传感器设计了一款能够实时检测人体血压、心率和体温的多功能健康检测系统。该系统由人体生理参数采集、数据分析处理、显示数据三部分组成,实现了对人体生理参数的实时采集显示和异常生理参数提醒的功能。 相似文献
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由于新冠肺炎疫情的影响,人们对于非接触式测温这种方式的需求大大增加。因此,本文主要描述了利用STM32单片机、按键、LCD显示屏、MLX90614非接触测温、蜂鸣器等模块制作出简易的非接触人体测温仪的设计,实现非接触红外线人体测温、非正常体温报警等功能的一体化。 相似文献
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面对传统节能控制系统电能耗费大、实训室管理不全面,导致节能控制效果较差的问题,提出基于OneNET云平台与物联网MQTT协议的智慧节能控制系统;选择HTML5-20工控板,支持MQTT协议,并与单片机通信;使用MLX90614型号红外温度传感器,通过探测辐射情况,实现高精度温度测量;设计HC-SR501人体红外感应模块,监控实训室设备安全使用情况,避免出现电能消耗大的情况;根据系统软件部分功能模块,通过手机app端移动设备控制教室设备,并随时监管设备运行状态;将远程智能控制接入OneNET平台,实现机构管理员管理、设备运行报表和自动检修功能;由系统测试结果可知,该系统风扇最少耗电为40 W、电灯最少耗电为0.1度,说明电能消耗较少;实训室温度和湿度均在正常监管范围内,说明实训室处于安全状态;该系统设计从节能、安全管理角度,解决实训室的智能管理问题,也为资产失窃防患提供保障. 相似文献
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Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets.This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations.A range of Neural Networks were developed for each local temperature, with topologies of 1–2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ±0.46 °C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ±1.2 °C, which is similar to the precision of existent average skin temperature models even though the average value is more stable. 相似文献
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Shogo Matsuno Tota Mizuno Hirotoshi Asano Kazuyuki Mito Naoaki Itakura 《Artificial Life and Robotics》2018,23(3):367-372
In this paper, we propose a novel method for evaluating mental workload (MWL) using variances in facial temperature. Moreover, our method aims to evaluate autonomic nerve activity using single facial thermal imaging. The autonomic nervous system is active under MWL. In previous studies, temperature differences between the nasal and forehead portions of the face were used in MWL evaluation and estimation. Hence, nasal skin temperature (NST) is said to be a reliable indicator of autonomic nerve activity. In addition, autonomic nerve activity has little effect on forehead temperature; thus, temperature differences between the nasal and forehead portions of the face have also been demonstrated to be a good indicator of autonomic nerve activity (along with other physiological indicators such as EEG and heart rate). However, these approaches have not considered temperature changes in other parts of the face. Thus, we propose novel method using variances in temperature for the entire face. Our proposed method enables capture of other parts of the face for temperature monitoring, thereby increasing evaluation and estimation accuracy at higher sensitivity levels than conventional methods. Finally, we also examined whether further high-precision evaluation and estimation was feasible. Our results proved that our proposed method is a highly accurate evaluation method compared with results obtained in previous studies using NST. 相似文献
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介绍了基于14位输出的智能磁敏元件 MLX90363制成的转角传感器,它利用STM32F103C8T6微控制器相应的电路作为数据处理模块,将 MLX90363芯片采集的双路角度数据读出,通过一定的算法处理,实现±720°范围的多圈角度计算,并利用CAN总线将采集到的数据输出.设计了转角传感器基本方案,包括传感器的系统结构、硬件电路和软件程序.给出了利用两路磁敏元件采集的转角信号计算绝对转角的算法,以及利用 CAN总线进行软件中断的方法,实现回正零点的设置.该传感器成本低、精确度高,可以应用于ESP和EPS系统中为汽车方向盘转角提供精确角度测量. 相似文献